Deep Learning Pytorch Second by Huang Howard (18 results)

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Hardcover. Condition: new. Hardcover. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorchs powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now. Project-based learning: Build an end-to-end medical image classifier that cements every concept. Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed. CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks. Generative models: Create text and images with large language models and diffusion networks. Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment. Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment. Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable. Finish the book able to design, train, and ship state-of-the-art models using PyTorchs flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward. Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning. In Deep Learning with PyTorch, Second Edition, youll learn how to create your own neural network and deep learning systems and take full advantage of PyTorchs built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. PyTorch makes it easy to build the powerful neural networks that underpin many modern advances in artificial intelligence. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Condition: New. 2nd. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorch's powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now.  Project-based learning: Build an end-to-end medical image classifier that cements every concept.  Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed.  CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks.  Generative models: Create text and images with large language models and diffusion networks.  Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment.  Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment.  Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable.  Finish the book able to design, train, and ship state-of-the-art models using PyTorch's flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward.  Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning.…

Hardback. Condition: New. New copy - Usually dispatched within 2 working days.

Condition: New.

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Hardback. Condition: New. 2nd. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorch's powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now.  Project-based learning: Build an end-to-end medical image classifier that cements every concept.  Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed.  CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks.  Generative models: Create text and images with large language models and diffusion networks.  Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment.  Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment.  Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable.  Finish the book able to design, train, and ship state-of-the-art models using PyTorch's flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward.  Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning.…

Condition: New. 2026. 2nd Edition. paperback. . . . . .

Condition: New.

Condition: New.

Buch. Condition: Neu. Neuware - In Deep Learning with PyTorch, Second Edition, you'll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch's built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. PyTorch makes it easy to build the powerful neural networks that underpin many modern advances in artificial intelligence. …

Condition: New. 2026. 2nd Edition. paperback. . . . . . Books ship from the US and Ireland.

Condition: NEW.

Hardcover. Condition: new. Hardcover. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorchs powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now. Project-based learning: Build an end-to-end medical image classifier that cements every concept. Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed. CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks. Generative models: Create text and images with large language models and diffusion networks. Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment. Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment. Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable. Finish the book able to design, train, and ship state-of-the-art models using PyTorchs flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward. Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning. In Deep Learning with PyTorch, Second Edition, youll learn how to create your own neural network and deep learning systems and take full advantage of PyTorchs built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. PyTorch makes it easy to build the powerful neural networks that underpin many modern advances in artificial intelligence. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardback. Condition: New. 2nd. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorch's powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now.  Project-based learning: Build an end-to-end medical image classifier that cements every concept.  Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed.  CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks.  Generative models: Create text and images with large language models and diffusion networks.  Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment.  Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment.  Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable.  Finish the book able to design, train, and ship state-of-the-art models using PyTorch's flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward.  Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning.…

Hardcover. Condition: new. Hardcover. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorchs powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now. Project-based learning: Build an end-to-end medical image classifier that cements every concept. Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed. CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks. Generative models: Create text and images with large language models and diffusion networks. Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment. Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment. Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable. Finish the book able to design, train, and ship state-of-the-art models using PyTorchs flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward. Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning. In Deep Learning with PyTorch, Second Edition, youll learn how to create your own neural network and deep learning systems and take full advantage of PyTorchs built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. PyTorch makes it easy to build the powerful neural networks that underpin many modern advances in artificial intelligence. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardback. Condition: New. 2nd. Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorch's powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now.  Project-based learning: Build an end-to-end medical image classifier that cements every concept.  Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed.  CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks.  Generative models: Create text and images with large language models and diffusion networks.  Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment.  Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment.  Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable.  Finish the book able to design, train, and ship state-of-the-art models using PyTorch's flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward.  Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning.…